afn.py

"""
@author: Baixu Chen
@contact: cbx_99_hasta@outlook.com
"""
import random
import time
import warnings
import sys
import argparse
import shutil
import os.path as osp

import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.optim import SGD
from torch.utils.data import DataLoader
import torch.nn.functional as F

import utils
from tllib.normalization.afn import AdaptiveFeatureNorm, ImageClassifier
from tllib.modules.entropy import entropy
import tllib.vision.models as models
from tllib.utils.data import ForeverDataIterator
from tllib.utils.metric import accuracy
from tllib.utils.meter import AverageMeter, ProgressMeter
from tllib.utils.logger import CompleteLogger
from tllib.utils.analysis import collect_feature, tsne, a_distance


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def main(args: argparse.Namespace):
    logger = CompleteLogger(args.log, args.phase)
    print(args)

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    cudnn.benchmark = True

    # Data loading code
    train_transform = utils.get_train_transform(args.train_resizing, random_horizontal_flip=True,
                                                random_color_jitter=False)
    val_transform = utils.get_val_transform(args.val_resizing)
    print("train_transform: ", train_transform)
    print("val_transform: ", val_transform)

    train_source_dataset, train_target_dataset, val_dataset, test_dataset, num_classes, args.class_names = \
        utils.get_dataset(args.data, args.root, args.source, args.target, train_transform, val_transform)
    train_source_loader = DataLoader(train_source_dataset, batch_size=args.batch_size,
                                     shuffle=True, num_workers=args.workers, drop_last=True)
    train_target_loader = DataLoader(train_target_dataset, batch_size=args.batch_size,
                                     shuffle=True, num_workers=args.workers, drop_last=True)
    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)
    test_loader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)

    train_source_iter = ForeverDataIterator(train_source_loader)
    train_target_iter = ForeverDataIterator(train_target_loader)

    # create model
    print("=> using pre-trained model '{}'".format(args.arch))
    backbone = utils.get_model(args.arch)
    pool_layer = nn.Identity() if args.no_pool else None
    backbone = models.__dict__[args.arch](pretrained=True)
    classifier = ImageClassifier(backbone, train_source_dataset.num_classes, args.num_blocks,
                                 bottleneck_dim=args.bottleneck_dim, dropout_p=args.dropout_p, pool_layer=pool_layer).to(device)
    adaptive_feature_norm = AdaptiveFeatureNorm(args.delta).to(device)

    # define optimizer
    # the learning rate is fixed according to origin paper
    optimizer = SGD(classifier.get_parameters(), args.lr, weight_decay=args.weight_decay)

    # resume from the best checkpoint
    if args.phase != 'train':
        checkpoint = torch.load(logger.get_checkpoint_path('best'), map_location='cpu')
        classifier.load_state_dict(checkpoint)

    # analysis the model
    if args.phase == 'analysis':
        # extract features from both domains
        feature_extractor = nn.Sequential(classifier.backbone, classifier.pool_layer, classifier.bottleneck).to(device)
        source_feature = collect_feature(train_source_loader, feature_extractor, device)
        target_feature = collect_feature(train_target_loader, feature_extractor, device)
        # plot t-SNE
        tSNE_filename = osp.join(logger.visualize_directory, 'TSNE.png')
        tsne.visualize(source_feature, target_feature, tSNE_filename)
        print("Saving t-SNE to", tSNE_filename)
        # calculate A-distance, which is a measure for distribution discrepancy
        A_distance = a_distance.calculate(source_feature, target_feature, device)
        print("A-distance =", A_distance)
        return

    if args.phase == 'test':
        acc1 = utils.validate(test_loader, classifier, args, device)
        print(acc1)
        return

    # start training
    best_acc1 = 0.
    for epoch in range(args.epochs):
        # train for one epoch
        train(train_source_iter, train_target_iter, classifier, adaptive_feature_norm, optimizer, epoch, args)

        # evaluate on validation set
        acc1 = utils.validate(val_loader, classifier, args, device)

        # remember best acc@1 and save checkpoint
        torch.save(classifier.state_dict(), logger.get_checkpoint_path('latest'))
        if acc1 > best_acc1:
            shutil.copy(logger.get_checkpoint_path('latest'), logger.get_checkpoint_path('best'))
        best_acc1 = max(acc1, best_acc1)

    print("best_acc1 = {:3.1f}".format(best_acc1))

    # evaluate on test set
    classifier.load_state_dict(torch.load(logger.get_checkpoint_path('best')))
    acc1 = utils.validate(test_loader, classifier, args, device)
    print("test_acc1 = {:3.1f}".format(acc1))

    logger.close()


def train(train_source_iter: ForeverDataIterator, train_target_iter: ForeverDataIterator, model: ImageClassifier,
          adaptive_feature_norm: AdaptiveFeatureNorm, optimizer: SGD, epoch: int, args: argparse.Namespace):
    batch_time = AverageMeter('Time', ':3.1f')
    data_time = AverageMeter('Data', ':3.1f')
    cls_losses = AverageMeter('Cls Loss', ':3.2f')
    norm_losses = AverageMeter('Norm Loss', ':3.2f')
    src_feature_norm = AverageMeter('Source Feature Norm', ':3.2f')
    tgt_feature_norm = AverageMeter('Target Feature Norm', ':3.2f')
    cls_accs = AverageMeter('Cls Acc', ':3.1f')
    tgt_accs = AverageMeter('Tgt Acc', ':3.1f')

    progress = ProgressMeter(
        args.iters_per_epoch,
        [batch_time, data_time, cls_losses, norm_losses, src_feature_norm, tgt_feature_norm, cls_accs, tgt_accs],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()

    end = time.time()
    for i in range(args.iters_per_epoch):
        x_s, labels_s = next(train_source_iter)
        x_t, labels_t = next(train_target_iter)

        x_s = x_s.to(device)
        x_t = x_t.to(device)
        labels_s = labels_s.to(device)
        labels_t = labels_t.to(device)

        # measure data loading time
        data_time.update(time.time() - end)

        # compute output
        y_s, f_s = model(x_s)
        y_t, f_t = model(x_t)

        # classification loss
        cls_loss = F.cross_entropy(y_s, labels_s)
        # norm loss
        norm_loss = adaptive_feature_norm(f_s) + adaptive_feature_norm(f_t)

        loss = cls_loss + norm_loss * args.trade_off_norm

        # using entropy minimization
        if args.trade_off_entropy:
            y_t = F.softmax(y_t, dim=1)
            entropy_loss = entropy(y_t, reduction='mean')
            loss += entropy_loss * args.trade_off_entropy

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # update statistics
        cls_acc = accuracy(y_s, labels_s)[0]
        tgt_acc = accuracy(y_t, labels_t)[0]

        cls_losses.update(cls_loss.item(), x_s.size(0))
        norm_losses.update(norm_loss.item(), x_s.size(0))
        src_feature_norm.update(f_s.norm(p=2, dim=1).mean().item(), x_s.size(0))
        tgt_feature_norm.update(f_t.norm(p=2, dim=1).mean().item(), x_s.size(0))
        cls_accs.update(cls_acc.item(), x_s.size(0))
        tgt_accs.update(tgt_acc.item(), x_s.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            progress.display(i)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='AFN for Partial Domain Adaptation')
    # dataset parameters
    parser.add_argument('root', metavar='DIR',
                        help='root path of dataset')
    parser.add_argument('-d', '--data', metavar='DATA', default='Office31', choices=utils.get_dataset_names(),
                        help='dataset: ' + ' | '.join(utils.get_dataset_names()) +
                             ' (default: Office31)')
    parser.add_argument('-s', '--source', help='source domain')
    parser.add_argument('-t', '--target', help='target domain')
    parser.add_argument('--train-resizing', type=str, default='default')
    parser.add_argument('--val-resizing', type=str, default='default')
    # model parameters
    parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
                        choices=utils.get_model_names(),
                        help='backbone architecture: ' +
                             ' | '.join(utils.get_model_names()) +
                             ' (default: resnet18)')
    parser.add_argument('--no-pool', action='store_true',
                        help='no pool layer after the feature extractor.')
    parser.add_argument('-n', '--num-blocks', default=1, type=int, help='Number of basic blocks for classifier')
    parser.add_argument('--bottleneck-dim', default=1000, type=int, help='Dimension of bottleneck')
    parser.add_argument('--dropout-p', default=0.5, type=float,
                        help='Dropout probability')
    # training parameters
    parser.add_argument('-b', '--batch-size', default=32, type=int,
                        metavar='N',
                        help='mini-batch size (default: 32)')
    parser.add_argument('--lr', '--learning-rate', default=0.001, type=float,
                        metavar='LR', help='initial learning rate', dest='lr')
    parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                        help='momentum')
    parser.add_argument('--wd', '--weight-decay', default=5e-4, type=float,
                        metavar='W', help='weight decay (default: 5e-4)',
                        dest='weight_decay')
    parser.add_argument('--trade-off-norm', default=0.05, type=float,
                        help='the trade-off hyper-parameter for norm loss')
    parser.add_argument('--trade-off-entropy', default=None, type=float,
                        help='the trade-off hyper-parameter for entropy loss')
    parser.add_argument('-r', '--delta', default=1, type=float, help='Increment for L2 norm')
    parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                        help='number of data loading workers (default: 4)')
    parser.add_argument('--epochs', default=20, type=int, metavar='N',
                        help='number of total epochs to run')
    parser.add_argument('-i', '--iters-per-epoch', default=500, type=int,
                        help='Number of iterations per epoch')
    parser.add_argument('-p', '--print-freq', default=100, type=int,
                        metavar='N', help='print frequency (default: 100)')
    parser.add_argument('--seed', default=None, type=int,
                        help='seed for initializing training. ')
    parser.add_argument('--per-class-eval', action='store_true',
                        help='whether output per-class accuracy during evaluation')
    parser.add_argument("--log", type=str, default='afn',
                        help="Where to save logs, checkpoints and debugging images.")
    parser.add_argument("--phase", type=str, default='train', choices=['train', 'test', 'analysis'],
                        help="When phase is 'test', only test the model."
                             "When phase is 'analysis', only analysis the model.")
    args = parser.parse_args()
    main(args)